The Role of Artificial Intelligence in the Healthcare Industry

Aayush Grover
The Innostation Publication
4 min readFeb 1, 2021

An innumerable amount of industries have had their dynamic noticeably changed thanks to Artificial Intelligence (AI) and Machine Learning (ML). Autonomous Vehicles, Neuro-Linguistic Programming (NLP), and Virtual Personal Assistants are all stunning examples of industries that have been vastly disrupted by the application of AI and ML. These practical and imaginative applications extend their reach into the field of medicine, enhancing our capabilities to predict, diagnose, and treat medical illnesses.

Predictive Analysis

Predictive Analysis is a large part of medicine in both diagnosis and treatment. Healthcare is largely oriented around inferring risk based upon current, and historical medical data. However, parallel to most aspects of medicine, such predictions are often subject to human error. If a patient were to visit the ER, healthcare professionals would be expected to make pre-diagnostic predictions that take into account the patient’s history and symptoms. These predictions are often constructed from generalities or educated guesses.

An expression I believe illustrates this perfectly is: “When you hear hoofbeats, think horses, not zebras.” What if we lived in a world in which we wouldn’t have to think horses? What if we could reliably base our predictions upon patient-specifics rather than generalities?

Machine Learning in Healthcare

Machine Learning is an aspect of Artificial Intelligence that improves itself based upon its experiences. Rather than explicit code in which we direct a machine to perform our commands, it adapts without distinct instruction. From a medical perspective, Machine Learning assists in predicting medical outcomes, patient-specific recovery-rate, and treatment plans holding the highest efficacy.

Increasing Diagnostic Capabilities

As we exhibited previously, we can essentially remove the guessing from our predictions. By analyzing statistics from past-records and complex patient medical history with inhuman speeds, we can rapidly increase our diagnostic capabilities. These Artificial Intelligence’s can outline day-to-day patterns to aid healthcare professionals in the wide-scale optimization of the healthcare industry.

Medical Models

Common, yet complex surgical practices such as reconstructive surgery can experience massive changes from medical modeling. Medical models are patient-specific anatomical models that aid in the preparation of surgery, and the comprehension of medical outcomes. ML-assisted healthcare would allow for us to create predictive models, aiding surgeons to outline patterns as well as potential risks of surgery. The drastic changes this could make on the foundation of reconstructive surgery and general surgery as well could soon exhibit profound results. For example, if a patient comes in with a newly diagnosed disease, predictive models could analyze certain factors such as their race, age, medical history, genetics, etc., and compare them to other cases of similar characteristics. This allows for a tailored treatment plan, optimizing it to fulfill the needs of the patient.

Supervised vs. Unsupervised Learning

Having already discussed the role that Artificial Intelligence and Machine Learning hold in the field of medicine, it’s important to understand technicalities for ML such as supervised/unsupervised learning, their differences, and their applications.

Supervised Learning

Supervised Machine Learning takes “labeled” data and feeds it into Artificial Intelligence, allowing it to learn from it. For example, if we were to show an AI a single medical case in which a patient dies of cancer due to a rare complication from a surgery, It would begin to associate this surgery with death. If thousands of these cases with the same surgery were fed into this AI, it would interpret that this surgery can cause a rare complication, rather than simply associating it with death. The AI exhibits its learning capabilities depending on how much it has been exposed to. As demonstrated previously, this holds great medical application in medical prediction. While analyzing data given to it, it’s capable of making predictions about unforeseen data.

Unsupervised Learning

Unsupervised Machine Learning works a bit differently. It deals with “unlabelled” data and isn’t fed information. In fact, it learns and discovers on its own. It can deal with more complex tasks in a better way than Supervised Machine Learning can, however, it has proven to have certain confounding factors and can be unpredictable at times. However, it uses techniques such as “clustering” and “association” to draw inferences and create conclusions. While Supervised Machine Learning is used in predictive analysis and demonstrated as incredibly useful time and time again, Unsupervised Machine Learning aids in learning about the data, as well as providing answers about a broader issue.

The Future of Medicine

With access to efficient diagnosis, accurate predictive models, and personalized treatment, the future of medicine shows great promise. AI and ML-assisted healthcare outline the future of the industry as a whole, improving the quality of care, efficiency of diagnosis, and treatment plans. Due to the massive role that these technologies play in the healthcare industry, the implementation of Artificial Intelligence and Machine Learning is our next step-forwards in saving lives.

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Aayush Grover
The Innostation Publication

Leveraging Artificial Intelligence and Blockchain technologies to propel societal transformation this decade.